Overview

Dataset statistics

Number of variables11
Number of observations24576
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

ROLE_TITLE is highly correlated with ROLE_CODEHigh correlation
ROLE_FAMILY_DESC is highly correlated with ROLE_TITLE and 1 other fieldsHigh correlation
ROLE_CODE is highly correlated with ROLE_TITLEHigh correlation
ROLE_ROLLUP_1 is highly correlated with ROLE_ROLLUP_2High correlation
ROLE_ROLLUP_2 is highly correlated with ROLE_ROLLUP_1High correlation
ID is uniformly distributed Uniform
ID has unique values Unique

Reproduction

Analysis started2022-10-15 03:03:44.232255
Analysis finished2022-10-15 03:04:05.952520
Duration21.72 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct24576
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16367.64929
Minimum0
Maximum32768
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.023463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1614.75
Q18149.75
median16403.5
Q324524.25
95-th percentile31135.25
Maximum32768
Range32768
Interquartile range (IQR)16374.5

Descriptive statistics

Standard deviation9464.173852
Coefficient of variation (CV)0.5782243793
Kurtosis-1.19855179
Mean16367.64929
Median Absolute Deviation (MAD)8181.5
Skewness-0.001474675394
Sum402251349
Variance89570586.71
MonotonicityNot monotonic
2022-10-15T08:34:06.094328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22701
 
< 0.1%
6961
 
< 0.1%
208711
 
< 0.1%
157061
 
< 0.1%
146451
 
< 0.1%
136681
 
< 0.1%
254031
 
< 0.1%
113571
 
< 0.1%
44351
 
< 0.1%
190281
 
< 0.1%
Other values (24566)24566
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
327681
< 0.1%
327661
< 0.1%
327651
< 0.1%
327641
< 0.1%
327631
< 0.1%
327621
< 0.1%
327611
< 0.1%
327601
< 0.1%
327591
< 0.1%
327571
< 0.1%

RESOURCE
Real number (ℝ≥0)

Distinct6469
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42881.13037
Minimum0
Maximum312153
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.186391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3853
Q120299
median35210
Q374189.25
95-th percentile81355
Maximum312153
Range312153
Interquartile range (IQR)53890.25

Descriptive statistics

Standard deviation34262.36267
Coefficient of variation (CV)0.7990079173
Kurtosis16.82170152
Mean42881.13037
Median Absolute Deviation (MAD)16792
Skewness2.820603172
Sum1053846660
Variance1173909496
MonotonicityNot monotonic
2022-10-15T08:34:06.258254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4675638
 
2.6%
79092347
 
1.4%
75078321
 
1.3%
25993317
 
1.3%
3853295
 
1.2%
75834226
 
0.9%
32270224
 
0.9%
6977223
 
0.9%
42085188
 
0.8%
1020179
 
0.7%
Other values (6459)21618
88.0%
ValueCountFrequency (%)
011
< 0.1%
386
< 0.1%
1361
 
< 0.1%
1382
 
< 0.1%
1538
< 0.1%
2033
 
< 0.1%
2163
 
< 0.1%
2331
 
< 0.1%
2372
 
< 0.1%
2564
 
< 0.1%
ValueCountFrequency (%)
3121531
 
< 0.1%
3121521
 
< 0.1%
3121401
 
< 0.1%
3121391
 
< 0.1%
3121321
 
< 0.1%
3121311
 
< 0.1%
3121304
< 0.1%
3121293
< 0.1%
3121221
 
< 0.1%
3121211
 
< 0.1%

MGR_ID
Real number (ℝ≥0)

Distinct3996
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25893.69328
Minimum25
Maximum311696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.338050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile1140
Q14564
median13441
Q341786
95-th percentile87834.75
Maximum311696
Range311671
Interquartile range (IQR)37222

Descriptive statistics

Standard deviation35746.79671
Coefficient of variation (CV)1.380521362
Kurtosis17.70545767
Mean25893.69328
Median Absolute Deviation (MAD)9774
Skewness3.353856286
Sum636363406
Variance1277833475
MonotonicityNot monotonic
2022-10-15T08:34:06.407011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
770111
 
0.5%
227066
 
0.3%
259463
 
0.3%
135056
 
0.2%
201454
 
0.2%
1685052
 
0.2%
539650
 
0.2%
780747
 
0.2%
1821346
 
0.2%
1868646
 
0.2%
Other values (3986)23985
97.6%
ValueCountFrequency (%)
2518
0.1%
2713
0.1%
305
 
< 0.1%
324
 
< 0.1%
3321
0.1%
369
< 0.1%
432
 
< 0.1%
462
 
< 0.1%
478
 
< 0.1%
554
 
< 0.1%
ValueCountFrequency (%)
31169613
0.1%
3116835
 
< 0.1%
3116821
 
< 0.1%
3116512
 
< 0.1%
3115971
 
< 0.1%
3114334
 
< 0.1%
3113552
 
< 0.1%
3113381
 
< 0.1%
3112511
 
< 0.1%
3112032
 
< 0.1%

ROLE_ROLLUP_1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct123
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116955.3704
Minimum4292
Maximum311178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.500677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4292
5-th percentile117902
Q1117961
median117961
Q3117961
95-th percentile119134
Maximum311178
Range306886
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10950.86747
Coefficient of variation (CV)0.09363287406
Kurtosis91.76494067
Mean116955.3704
Median Absolute Deviation (MAD)0
Skewness-6.118413445
Sum2874295184
Variance119921498.4
MonotonicityNot monotonic
2022-10-15T08:34:06.578968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11796115994
65.1%
117902562
 
2.3%
91261544
 
2.2%
118315379
 
1.5%
118212309
 
1.3%
118290299
 
1.2%
119062275
 
1.1%
118887252
 
1.0%
118169230
 
0.9%
117916225
 
0.9%
Other values (113)5507
 
22.4%
ValueCountFrequency (%)
429210
 
< 0.1%
5110141
 
0.6%
1114622
 
0.1%
91261544
2.2%
117876131
 
0.5%
11788214
 
0.1%
11788773
 
0.3%
117890177
 
0.7%
11789363
 
0.3%
117902562
2.3%
ValueCountFrequency (%)
3111782
 
< 0.1%
2479529
 
< 0.1%
21670510
< 0.1%
2094341
 
< 0.1%
2032091
 
< 0.1%
1924414
 
< 0.1%
1837233
 
< 0.1%
1472361
 
< 0.1%
13879824
0.1%
1328397
 
< 0.1%

ROLE_ROLLUP_2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct168
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118260.8927
Minimum23779
Maximum286791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.657168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23779
5-th percentile117936
Q1118102
median118300
Q3118386
95-th percentile119256
Maximum286791
Range263012
Interquartile range (IQR)284

Descriptive statistics

Standard deviation4841.345712
Coefficient of variation (CV)0.04093784175
Kurtosis354.6126288
Mean118260.8927
Median Absolute Deviation (MAD)86
Skewness-13.55252197
Sum2906379700
Variance23438628.3
MonotonicityNot monotonic
2022-10-15T08:34:06.719653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1183003342
 
13.6%
1183432993
 
12.2%
1183271960
 
8.0%
1182251895
 
7.7%
1183861321
 
5.4%
1180521238
 
5.0%
1179621162
 
4.7%
118413960
 
3.9%
118446722
 
2.9%
118026544
 
2.2%
Other values (158)8439
34.3%
ValueCountFrequency (%)
2377923
 
0.1%
3101037
 
0.2%
117877131
 
0.5%
11788311
 
< 0.1%
117891113
 
0.5%
11789463
 
0.3%
117903372
1.5%
11791198
 
0.4%
11791761
 
0.2%
11791947
 
0.2%
ValueCountFrequency (%)
2867911
 
< 0.1%
1597168
 
< 0.1%
1511107
 
< 0.1%
1472371
 
< 0.1%
1452486
 
< 0.1%
1411764
 
< 0.1%
1405501
 
< 0.1%
13879924
0.1%
1328401
 
< 0.1%
1325641
 
< 0.1%

ROLE_DEPTNAME
Real number (ℝ≥0)

Distinct440
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118854.6591
Minimum4674
Maximum286792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.805239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4674
5-th percentile117878
Q1118391
median118910
Q3120428
95-th percentile125016
Maximum286792
Range282118
Interquartile range (IQR)2037

Descriptive statistics

Standard deviation18639.57457
Coefficient of variation (CV)0.156826621
Kurtosis39.96228982
Mean118854.6591
Median Absolute Deviation (MAD)969
Skewness-0.375063125
Sum2920972102
Variance347433740.2
MonotonicityNot monotonic
2022-10-15T08:34:06.888789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117878856
 
3.5%
117941596
 
2.4%
118514467
 
1.9%
117945465
 
1.9%
117920454
 
1.8%
117884424
 
1.7%
118403410
 
1.7%
119598409
 
1.7%
119181400
 
1.6%
120722383
 
1.6%
Other values (430)19712
80.2%
ValueCountFrequency (%)
467432
 
0.1%
548824
 
0.1%
56067
 
< 0.1%
610443
0.2%
672579
0.3%
76466
 
< 0.1%
1623262
0.3%
1966664
0.3%
1977296
0.4%
208072
 
< 0.1%
ValueCountFrequency (%)
2867921
 
< 0.1%
27769386
0.3%
2756007
 
< 0.1%
2742418
 
< 0.1%
2722831
 
< 0.1%
2539658
 
< 0.1%
2407663
 
< 0.1%
22501015
 
0.1%
2159203
 
< 0.1%
2040543
 
< 0.1%

ROLE_TITLE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct331
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125661.4926
Minimum117879
Maximum311867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:06.969558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum117879
5-th percentile117885
Q1118274
median118568
Q3120006
95-th percentile135809
Maximum311867
Range193988
Interquartile range (IQR)1732

Descriptive statistics

Standard deviation30491.34304
Coefficient of variation (CV)0.2426466725
Kurtosis24.99307548
Mean125661.4926
Median Absolute Deviation (MAD)604
Skewness5.074521184
Sum3088256842
Variance929722000.1
MonotonicityNot monotonic
2022-10-15T08:34:07.057554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1183213456
 
14.1%
1179052701
 
11.0%
1187841368
 
5.6%
117879963
 
3.9%
118568800
 
3.3%
117885606
 
2.5%
118054567
 
2.3%
118685429
 
1.7%
118777419
 
1.7%
118451400
 
1.6%
Other values (321)12867
52.4%
ValueCountFrequency (%)
117879963
 
3.9%
117885606
 
2.5%
117896126
 
0.5%
117899181
 
0.7%
1179052701
11.0%
117946252
 
1.0%
11798513
 
0.1%
11802868
 
0.3%
118043185
 
0.8%
11804710
 
< 0.1%
ValueCountFrequency (%)
3118673
 
< 0.1%
3108251
 
< 0.1%
307024351
1.4%
2995595
 
< 0.1%
2975601
 
< 0.1%
280788269
1.1%
2794824
 
< 0.1%
27330825
 
0.1%
2706901
 
< 0.1%
2686081
 
< 0.1%

ROLE_FAMILY_DESC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2183
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169860.2845
Minimum4673
Maximum311867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:07.138515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4673
5-th percentile117906
Q1117906
median128628
Q3233714
95-th percentile306795
Maximum311867
Range307194
Interquartile range (IQR)115808

Descriptive statistics

Standard deviation69329.22149
Coefficient of variation (CV)0.408154394
Kurtosis-0.6219345289
Mean169860.2845
Median Absolute Deviation (MAD)10722
Skewness1.005623488
Sum4174486352
Variance4806540952
MonotonicityNot monotonic
2022-10-15T08:34:07.460387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1179065184
 
21.1%
240983937
 
3.8%
117913513
 
2.1%
279443473
 
1.9%
117886399
 
1.6%
130134315
 
1.3%
117897268
 
1.1%
117879255
 
1.0%
168365246
 
1.0%
133686242
 
1.0%
Other values (2173)15744
64.1%
ValueCountFrequency (%)
467313
 
0.1%
625872
 
< 0.1%
117879255
 
1.0%
117886399
 
1.6%
117897268
 
1.1%
11789949
 
0.2%
1179053
 
< 0.1%
1179065184
21.1%
117913513
 
2.1%
1179379
 
< 0.1%
ValueCountFrequency (%)
3118672
 
< 0.1%
3118393
 
< 0.1%
3118342
 
< 0.1%
3117921
 
< 0.1%
3117821
 
< 0.1%
3117782
 
< 0.1%
3117468
 
< 0.1%
31170115
 
0.1%
3116359
 
< 0.1%
311622147
0.6%

ROLE_FAMILY
Real number (ℝ≥0)

Distinct64
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183598.0446
Minimum3130
Maximum308574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:07.547077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3130
5-th percentile19721
Q1118363
median119095
Q3290919
95-th percentile292795
Maximum308574
Range305444
Interquartile range (IQR)172556

Descriptive statistics

Standard deviation100563.0915
Coefficient of variation (CV)0.5477350903
Kurtosis-1.484153967
Mean183598.0446
Median Absolute Deviation (MAD)99374
Skewness-0.07835519969
Sum4512105543
Variance1.011293537 × 1010
MonotonicityNot monotonic
2022-10-15T08:34:07.627252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2909198278
33.7%
1184242028
 
8.3%
197212016
 
8.2%
1178871775
 
7.2%
292795968
 
3.9%
118398962
 
3.9%
308574942
 
3.8%
118453712
 
2.9%
118331660
 
2.7%
118638578
 
2.4%
Other values (54)5657
23.0%
ValueCountFrequency (%)
3130109
 
0.4%
4673271
 
1.1%
672569
 
0.3%
197212016
8.2%
19793279
 
1.1%
1178871775
7.2%
118131125
 
0.5%
118205334
 
1.4%
118295363
 
1.5%
118331660
 
2.7%
ValueCountFrequency (%)
308574942
 
3.8%
292795968
 
3.9%
2909198278
33.7%
270488523
 
2.1%
2543952
 
< 0.1%
249618162
 
0.7%
1611001
 
< 0.1%
1551733
 
< 0.1%
1512777
 
< 0.1%
1493532
 
< 0.1%

ROLE_CODE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct331
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119765.3124
Minimum117880
Maximum270691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.1 KiB
2022-10-15T08:34:07.705454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum117880
5-th percentile117888
Q1118209
median118570
Q3119353
95-th percentile125795
Maximum270691
Range152811
Interquartile range (IQR)1144

Descriptive statistics

Standard deviation5559.507074
Coefficient of variation (CV)0.04642001063
Kurtosis270.1023039
Mean119765.3124
Median Absolute Deviation (MAD)515
Skewness13.5419255
Sum2943352317
Variance30908118.9
MonotonicityNot monotonic
2022-10-15T08:34:07.783559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1183223456
 
14.1%
1179082701
 
11.0%
1187861368
 
5.6%
117880963
 
3.9%
118570800
 
3.3%
117888606
 
2.5%
118055567
 
2.3%
118687429
 
1.7%
118779419
 
1.7%
118454400
 
1.6%
Other values (321)12867
52.4%
ValueCountFrequency (%)
117880963
 
3.9%
117888606
 
2.5%
117898126
 
0.5%
117900181
 
0.7%
1179082701
11.0%
117948252
 
1.0%
117973273
 
1.1%
11798713
 
0.1%
11803068
 
0.3%
118046185
 
0.8%
ValueCountFrequency (%)
2706911
 
< 0.1%
2686101
 
< 0.1%
2668631
 
< 0.1%
2584364
< 0.1%
2543962
 
< 0.1%
2476605
< 0.1%
2390041
 
< 0.1%
2168275
< 0.1%
2121942
 
< 0.1%
2081271
 
< 0.1%

ACTION
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.1 KiB
1
23148 
0
 
1428

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24576
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
123148
94.2%
01428
 
5.8%

Length

2022-10-15T08:34:07.844892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-15T08:34:07.914804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
123148
94.2%
01428
 
5.8%

Most occurring characters

ValueCountFrequency (%)
123148
94.2%
01428
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123148
94.2%
01428
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common24576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123148
94.2%
01428
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII24576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123148
94.2%
01428
 
5.8%

Interactions

2022-10-15T08:34:04.802322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.238143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.039323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.917131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.716353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.530782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.319885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.350702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.184768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.997455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.899928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.322077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.122192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.984492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.797722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.601518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.414258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.449820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.272394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.079995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.980770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.397385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.197636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.072081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.880843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.682991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.484097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.528932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.349594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.152322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.071562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.480993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.297461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.150306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.964112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.753424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.566769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.616912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.417924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.246675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.147443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.555248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.401995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.231117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.050441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.832970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.649326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.695409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.497804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.330796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.233241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.634979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.483085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.314130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.119580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.914265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.952718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.768097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.582873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.409409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.329217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.716809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.566557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.381983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.205526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.997452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.030936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.864244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.666877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.485783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.414130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.804126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.655686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.480820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.281038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.080790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.111807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.947434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.750418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.572340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.500358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.880963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.749914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.548190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.364083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.164152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.181374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.028484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.835654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.655701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:05.583116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:57.959220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:58.835923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:33:59.638973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:00.432794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:01.233097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:02.268012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.114162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:03.914085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T08:34:04.731017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-15T08:34:07.968508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-15T08:34:08.063701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-15T08:34:08.175801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-15T08:34:08.277792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-15T08:34:05.721393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-15T08:34:05.873304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IDRESOURCEMGR_IDROLE_ROLLUP_1ROLE_ROLLUP_2ROLE_DEPTNAMEROLE_TITLEROLE_FAMILY_DESCROLE_FAMILYROLE_CODEACTION
02270750782550371183151183161182021187842620952909191187861
16967932331201179611183001203121203131203141184241203151
2135143495882431185551181781183201179051179062909191179081
3134003937175201179611183431247251179052409832909191179081
4670339330172901179611183861185221179051179062909191179081
5246711581820171179611183271216451248861471441186431248881
614512563817551179611179621192231257931467491186431257951
7421533235169731179611183001249421179051179062909191179081
8118223993949241179611183001201441180541243561178871180551
91153780765256071179611183431188561183211179062909191183221

Last rows

IDRESOURCEMGR_IDROLE_ROLLUP_1ROLE_ROLLUP_2ROLE_DEPTNAMEROLE_TITLEROLE_FAMILY_DESCROLE_FAMILYROLE_CODEACTION
24566168503926255091179611183431234541187841179062909191187861
24567626535625197171179611179621183521183211179062909191183220
24568221181975175511179611180521188671182591179062909191182611
24569112842029287977117926117927117884118568281735197211185701
245701196474226513721179611183431206661187772794433085741187791
245712157597143081179611183431188331188343091231184241188361
24572298021020173861179611184461190641206901308872909191206921
24573539040474322421179611183271219791179051179062909191179081
245748602555366400117910117911117920123191123191197211231921
24575157953496352925117980117981117920117879117886197211178801